AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
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课程信息
You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.
您将学到的内容有
Walk through examples of prognostic tasks
Apply tree-based models to estimate patient survival rates
Navigate practical challenges in medicine like missing data
您将获得的技能
- Deep Learning
- Machine Learning
- time-to-event modeling
- Random Forest
- model tuning
You’re comfortable with Python programming, statistics, and probability. The Deep Learning Specialization is recommended but not required.
提供方

deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
授课大纲 - 您将从这门课程中学到什么
Linear Prognostic Models
Build a linear prognostic model using logistic regression, then evaluate the model by calculating the concordance index. Finally, improve the model by adding feature interactions.
Prognosis with Tree-based Models
Tune decision tree and random forest models to predict the risk of a disease. Evaluate the model performance using the c-index. Identify missing data and how it may alter the data distribution, then use imputation to fill in missing data, in order to improve model performance.
Survival Models and Time
This week, you will work with data where the time that a disease occurs is a variable. Instead of predicting just the 10-year risk of a disease, you will build more flexible models that can predict the 5 year, 7 year, or 10 year risk.
Build a Risk Model Using Linear and Tree-based Models
This week, you will fit a linear model, and a tree-based risk model on survival data, to customize a risk score for each patient, based on their health profile. The risk score represents the patient’s relative risk of getting a particular disease. You will then evaluate each model’s performance by implementing and using a concordance index that incorporates time to event and censored data.
审阅
- 5 stars78.16%
- 4 stars16.33%
- 3 stars3.23%
- 2 stars1.54%
- 1 star0.70%
来自AI FOR MEDICAL PROGNOSIS 的热门评论
Excellent course! Real world data and robust models. Of particular value was the implementation of the SHAP feature interpretation algorithm as applied to ensemble models.
This course was great and more challenging that I have expected. More focus on statistics and survival data which is important for prognosis. Course has a good flow and valuable content.
the coding assignments were not that hard sadly, but the knowledge about techniques and methods and formulas to interpret the prognosis is very helpful
This course enabled me to apply machine learning for prognosis related scenario & learn multiple risk assessment related scenario.
关于 AI for Medicine 专项课程
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.

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